{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PySDR接收信号初探\n",
    "\n",
    "作者：Mindstorm (brainstorm233@qq.com)\n",
    "\n",
    "时间：2024.5.5\n",
    "\n",
    "参考：\n",
    "1. PlutoSDR in Python https://pysdr.org/content/pluto.html\n",
    "2. ChatGPT4 http://chat.openai.com/\n",
    "3. ADALM-PLUTO Overview https://wiki.analog.com/university/tools/pluto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 【例程一】测试连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 若返回一组复数向量，则pluto正常工作\n",
    "import adi\n",
    "sdr = adi.Pluto('ip:192.168.2.1') # or whatever your Pluto's IP is\n",
    "sdr.sample_rate = int(2.5e6)\n",
    "sdr.rx()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 【例程二】采集信号并打印前十个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import adi\n",
    "\n",
    "sample_rate = 1e6 # Hz\n",
    "center_freq = 100e6 # Hz\n",
    "num_samps = 10000 # number of samples returned per call to rx()\n",
    "\n",
    "sdr = adi.Pluto('ip:192.168.2.1')\n",
    "sdr.gain_control_mode_chan0 = 'manual'\n",
    "sdr.rx_hardwaregain_chan0 = 70.0 # dB\n",
    "sdr.rx_lo = int(center_freq)\n",
    "sdr.sample_rate = int(sample_rate)\n",
    "sdr.rx_rf_bandwidth = int(sample_rate) # filter width, just set it to the same as sample rate for now\n",
    "sdr.rx_buffer_size = num_samps\n",
    "\n",
    "samples = sdr.rx() # receive samples off Pluto\n",
    "print(samples[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 【例程三】边发边收一个QPSK信号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import adi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sample_rate = 1e6 # Hz\n",
    "center_freq = 915e6 # Hz\n",
    "num_samps = 100000 # number of samples per call to rx()\n",
    "\n",
    "sdr = adi.Pluto(\"ip:192.168.2.1\")\n",
    "sdr.sample_rate = int(sample_rate)\n",
    "\n",
    "# Config Tx\n",
    "sdr.tx_rf_bandwidth = int(sample_rate) # filter cutoff, just set it to the same as sample rate\n",
    "sdr.tx_lo = int(center_freq)\n",
    "sdr.tx_hardwaregain_chan0 = -50 # Increase to increase tx power, valid range is -90 to 0 dB\n",
    "\n",
    "# Config Rx\n",
    "sdr.rx_lo = int(center_freq)\n",
    "sdr.rx_rf_bandwidth = int(sample_rate)\n",
    "sdr.rx_buffer_size = num_samps\n",
    "sdr.gain_control_mode_chan0 = 'manual'\n",
    "sdr.rx_hardwaregain_chan0 = 0.0 # dB, increase to increase the receive gain, but be careful not to saturate the ADC\n",
    "\n",
    "# Create transmit waveform (QPSK, 16 samples per symbol)\n",
    "num_symbols = 1000\n",
    "x_int = np.random.randint(0, 4, num_symbols) # 0 to 3\n",
    "x_degrees = x_int*360/4.0 + 45 # 45, 135, 225, 315 degrees\n",
    "x_radians = x_degrees*np.pi/180.0 # sin() and cos() takes in radians\n",
    "x_symbols = np.cos(x_radians) + 1j*np.sin(x_radians) # this produces our QPSK complex symbols\n",
    "samples = np.repeat(x_symbols, 16) # 16 samples per symbol (rectangular pulses)\n",
    "samples *= 2**14 # The PlutoSDR expects samples to be between -2^14 and +2^14, not -1 and +1 like some SDRs\n",
    "\n",
    "# Start the transmitter\n",
    "sdr.tx_cyclic_buffer = True # Enable cyclic buffers\n",
    "sdr.tx(samples) # start transmitting\n",
    "\n",
    "# Clear buffer just to be safe\n",
    "for i in range (0, 10):\n",
    "    raw_data = sdr.rx()\n",
    "\n",
    "# Receive samples\n",
    "rx_samples = sdr.rx()\n",
    "print(rx_samples)\n",
    "\n",
    "# Stop transmitting\n",
    "sdr.tx_destroy_buffer()\n",
    "\n",
    "# Calculate power spectral density (frequency domain version of signal)\n",
    "psd = np.abs(np.fft.fftshift(np.fft.fft(rx_samples)))**2\n",
    "psd_dB = 10*np.log10(psd)\n",
    "f = np.linspace(sample_rate/-2, sample_rate/2, len(psd))\n",
    "\n",
    "# Plot time domain\n",
    "plt.figure(0)\n",
    "plt.plot(np.real(rx_samples[::100]))\n",
    "plt.plot(np.imag(rx_samples[::100]))\n",
    "plt.xlabel(\"Time\")\n",
    "\n",
    "# Plot freq domain\n",
    "plt.figure(1)\n",
    "plt.plot(f/1e6, psd_dB)\n",
    "plt.xlabel(\"Frequency [MHz]\")\n",
    "plt.ylabel(\"PSD\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示频谱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import adi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def receive_and_plot_power_spectrum(start_freq, end_freq):\n",
    "    sample_rate = end_freq - start_freq  # Hz\n",
    "    if sample_rate >= 61.44e6:\n",
    "        raise ValueError('Sample Rate must be less than 61.44MHz!')\n",
    "    if sample_rate <= 0:\n",
    "        raise ValueError('Sample Rate must not be a negative number!')\n",
    "    center_freq = (start_freq + end_freq) / 2  # Center frequency\n",
    "    num_samps = 100000  # number of samples per call to rx()\n",
    "\n",
    "    sdr = adi.Pluto(\"ip:192.168.2.1\")\n",
    "    sdr.sample_rate = int(sample_rate)\n",
    "\n",
    "    # Config Rx\n",
    "    sdr.rx_lo = int(center_freq)\n",
    "    sdr.rx_rf_bandwidth = int(sample_rate)\n",
    "    sdr.rx_buffer_size = num_samps\n",
    "    sdr.gain_control_mode_chan0 = 'manual'\n",
    "    sdr.rx_hardwaregain_chan0 = 0.0  # dB, increase to increase the receive gain, but be careful not to saturate the ADC\n",
    "\n",
    "    # Clear buffer just to be safe\n",
    "    for i in range(0, 10):\n",
    "        raw_data = sdr.rx()\n",
    "\n",
    "    # Receive samples\n",
    "    rx_samples = sdr.rx()\n",
    "\n",
    "    # Calculate power spectral density (frequency domain version of signal)\n",
    "    psd = np.abs(np.fft.fftshift(np.fft.fft(rx_samples))) ** 2\n",
    "    psd_dB = 10 * np.log10(psd)\n",
    "    f = np.linspace(start_freq, end_freq, len(psd))\n",
    "\n",
    "    # Plot freq domain\n",
    "    plt.figure(0)\n",
    "    plt.plot(f / 1e6, psd)\n",
    "    plt.xlabel(\"Frequency [MHz]\")\n",
    "    plt.ylabel(\"PSD\")\n",
    "    plt.show()\n",
    "\n",
    "    # Plot freq domain\n",
    "    plt.figure(1)\n",
    "    plt.plot(f / 1e6, psd_dB)\n",
    "    plt.xlabel(\"Frequency [MHz]\")\n",
    "    plt.ylabel(\"PSD in dB\")\n",
    "    plt.show()\n",
    "\n",
    "# Example usage\n",
    "start_frequency = 910e6\n",
    "end_frequency = 970e6\n",
    "receive_and_plot_power_spectrum(start_frequency, end_frequency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 存储频谱到.mat文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import adi\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import savemat\n",
    "from datetime import datetime\n",
    "\n",
    "def receive_and_plot_power_spectrum(start_freq, end_freq):\n",
    "    sample_rate = end_freq - start_freq  # Hz\n",
    "    if sample_rate >= 61.44e6:\n",
    "        raise ValueError('Sample Rate must be less than 61.44MHz!')\n",
    "    if sample_rate <= 0:\n",
    "        raise ValueError('Sample Rate must not be a negative number!')\n",
    "    center_freq = (start_freq + end_freq) / 2  # Center frequency\n",
    "    num_samps = 100000  # number of samples per call to rx()\n",
    "\n",
    "    sdr = adi.Pluto(\"ip:192.168.2.1\")\n",
    "    sdr.sample_rate = int(sample_rate)\n",
    "\n",
    "    # Config Rx\n",
    "    sdr.rx_lo = int(center_freq)\n",
    "    sdr.rx_rf_bandwidth = int(sample_rate)\n",
    "    sdr.rx_buffer_size = num_samps\n",
    "    sdr.gain_control_mode_chan0 = 'manual'\n",
    "    sdr.rx_hardwaregain_chan0 = 0.0  # dB, increase to increase the receive gain, but be careful not to saturate the ADC\n",
    "\n",
    "    # Clear buffer just to be safe\n",
    "    for i in range(0, 10):\n",
    "        raw_data = sdr.rx()\n",
    "\n",
    "    # Receive samples\n",
    "    rx_samples = sdr.rx()\n",
    "\n",
    "    # Calculate power spectral density (frequency domain version of signal)\n",
    "    psd = np.abs(np.fft.fftshift(np.fft.fft(rx_samples))) ** 2\n",
    "    psd_dB = 10 * np.log10(psd)\n",
    "    f = np.linspace(start_freq, end_freq, len(psd))\n",
    "\n",
    "    # Plot freq domain\n",
    "    plt.figure(0)\n",
    "    plt.plot(f / 1e6, psd)\n",
    "    plt.xlabel(\"Frequency [MHz]\")\n",
    "    plt.ylabel(\"PSD\")\n",
    "    plt.show()\n",
    "\n",
    "    # Plot freq domain\n",
    "    plt.figure(1)\n",
    "    plt.plot(f / 1e6, psd_dB)\n",
    "    plt.xlabel(\"Frequency [MHz]\")\n",
    "    plt.ylabel(\"PSD in dB\")\n",
    "    plt.show()\n",
    "\n",
    "    # Save data to .mat file\n",
    "    timestamp = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "    filename = timestamp + \".mat\"\n",
    "    savemat(filename, {'f': f, 'psd': psd, 'psd_dB': psd_dB})\n",
    "\n",
    "# Example usage\n",
    "start_frequency = 910e6\n",
    "end_frequency = 970e6\n",
    "receive_and_plot_power_spectrum(start_frequency, end_frequency)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pySDR",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
